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Data Engineer
Traffic Label LimitedData Engineer at Traffic Label building data pipelines and integrating with third-party systems. Involves working with Snowflake and AWS Lambda for data transformation and ingestion.
Tech Stack
Tools & technologiesAirflowAWSETLPythonSQLTerraform
About the role
Key responsibilities & impact- Build and maintain ingestion pipelines (REST APIs, SFTP, database replication) into Snowflake
- Write dbt models across bronze/silver/gold layers following established conventions
- Integrate with third-party ESPs (DotDigital, Adestra, FastTrack): push segments, pull back campaign events (opens, clicks, bounces, conversions)
- Develop and maintain AWS Lambda functions for data extraction and reverse ETL
- Build data models for identity resolution, segmentation, and attribution
- Configure and manage Airflow DAGs for orchestration
- Implement data quality checks, monitoring, and alerting
- Manage Snowflake and AWS infrastructure via Terraform
- Investigate and resolve data incidents (pipeline failures, data quality issues, PII exposure)
Requirements
What you’ll need- 4+ years of experience developing and deploying data pipelines using Python in production (tested, CI/CD, error handling)
- 4+ years of experience writing complex SQL queries, performance tuning, Snowflake-optimised
- Strong experience with Snowflake: SnowPipe, external stages, RBAC, masking policies, warehouses
- Strong experience with dbt Core for data transformation, testing, and incremental models
- Experience with data warehouse design: star schemas, snowflake schemas, dimensions, facts, slowly changing dimensions
- Experience developing and deploying ETL/ELT pipelines using Airflow or similar orchestration tools
- AI-native development workflow: daily use of AI coding tools (Copilot, Cursor, Claude Code, Kiro) for code generation, review, and debugging
Benefits
Comp & perks- Competitive salary
- Flexible working environment and supportive team culture
ATS Keywords
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Hard Skills & Tools
PythonSQLSnowflakedbtETLELTAWS LambdaAirflowTerraformdata modeling